Life Sciences Commons^™

Extended Functional Connectivity Of Convergent Structural Alterations Among Individuals With Ptsd: A Neuroimaging Meta-Analysis, Brianna S. Pankey, Michael C. Riedel, Isis Cowan, Jessica E. Bartley, Rosario Pintos Lobo, Lauren D. Hill-Bowen, Taylor Sato, Erica D. Musser, Matthew T. Sutherland, Angela R. Laird

Psychology Faculty Publications

Background: Post-traumatic stress disorder (PTSD) is a debilitating disorder defined by the onset of intrusive, avoidant, negative cognitive or affective, and/or hyperarousal symptoms after witnessing or experiencing a traumatic event. Previous voxel-based morphometry studies have provided insight into structural brain alterations associated with PTSD with notable heterogeneity across these studies. Furthermore, how structural alterations may be associated with brain function, as measured by task-free and task-based functional connectivity, remains to be elucidated.

Methods: Using emergent meta-analytic techniques, we sought to first identify a consensus of structural alterations in PTSD using the anatomical likelihood estimation (ALE) approach. Next, we generated functional …

Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho

SMU Data Science Review

Breast cancer is prevalent among women in the United States. Breast cancer screening is standard but requires a radiologist to review screening images to make a diagnosis. Diagnosis through the traditional screening method of mammography currently has an accuracy of about 78% for women of all ages and demographics. A more recent and precise technique called Digital Breast Tomosynthesis (DBT) has shown to be more promising but is less well studied. A machine learning model trained on DBT images has the potential to increase the success of identifying breast cancer and reduce the time it takes to diagnose a patient, …

Bcc Skin Cancer Diagnosis Based On Texture Analysis Techniques, Shao-Hui Chuang, Xiaoyan Sun, Wen-Yu Chang, Gwo-Shing Chen, Adam Huang, Jiang Li, Frederic D. Mckenzie

Electrical & Computer Engineering Faculty Publications

In this paper, we present a texture analysis based method for diagnosing the Basal Cell Carcinoma (BCC) skin cancer using optical images taken from the suspicious skin regions. We first extracted the Run Length Matrix and Haralick texture features from the images and used a feature selection algorithm to identify the most effective feature set for the diagnosis. We then utilized a Multi-Layer Perceptron (MLP) classifier to classify the images to BCC or normal cases. Experiments showed that detecting BCC cancer based on optical images is feasible. The best sensitivity and specificity we achieved on our data set were 94% …

The Human In 3d: Advanced Morphometric Analysis Of High-Resolution Anatomically Accurate Computed Models, Summer J. Decker

USF Tampa Graduate Theses and Dissertations

Computed virtual models of anatomical structures are proving to be of increasing value in clinical medicine, education and research. With a variety of fields focused on craniofacial and pelvic anatomy there is a need for accurate anatomical models. Recent technological advancements in computer and medical imaging technologies have provided the tools necessary to develop three-dimensional (3D) functional models of human anatomy for use in medicine (surgical planning and education), forensics and engineering (computer-aided design (CAD) and finite element analysis).

Traditionally caliper methodologies are used in the quantitative analysis of human anatomy. In order for experts in anatomy and morphometrics to …